valueBox(
value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
caption = "Total confirmed cases",
icon = "fas fa-user-md",
color = confirmed_color
)valueBox(
value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
"%)",
sep = ""
),
caption = "Death cases (death rate)",
icon = "fas fa-heart-broken",
color = death_color
)plotly::plot_ly(data = df_daily) %>%
plotly::add_trace(
x = ~date,
# y = ~active_cum,
y = ~confirmed_cum,
type = "scatter",
mode = "lines+markers",
# name = "Active",
name = "Confirmed",
line = list(color = active_color),
marker = list(color = active_color)
) %>%
plotly::add_trace(
x = ~date,
y = ~death_cum,
type = "scatter",
mode = "lines+markers",
name = "Death",
line = list(color = death_color),
marker = list(color = death_color)
) %>%
plotly::add_annotations(
x = as.Date("2020-03-08"),
y = 3,
text = paste("First case"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::add_annotations(
x = as.Date("2020-03-18"),
y = 1,
text = paste("First death"),
xref = "x",
yref = "y",
arrowhead = 5,
arrowhead = 3,
arrowsize = 1,
showarrow = TRUE,
ax = -10,
ay = -90
) %>%
plotly::layout(
title = "",
yaxis = list(title = "Cumulative number of cases"),
xaxis = list(title = "Date"),
legend = list(x = 0.1, y = 0.9),
hovermode = "compare"
)daily_confirmed <- coronavirus %>%
dplyr::filter(type == "confirmed") %>%
dplyr::filter(date >= "2020-02-29") %>%
dplyr::mutate(country = Country.Region) %>%
dplyr::group_by(date, country) %>%
dplyr::summarise(total = sum(cases)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from = country, values_from = total)
#----------------------------------------
# Plotting the data
daily_confirmed %>%
plotly::plot_ly() %>%
plotly::add_trace(
x = ~date,
y = ~Bangladesh,
type = "scatter",
mode = "lines+markers",
name = "Bangladesh"
) %>%
plotly::add_trace(
x = ~date,
y = ~France,
type = "scatter",
mode = "lines+markers",
name = "France"
) %>%
plotly::add_trace(
x = ~date,
y = ~Spain,
type = "scatter",
mode = "lines+markers",
name = "Spain"
) %>%
plotly::add_trace(
x = ~date,
y = ~Italy,
type = "scatter",
mode = "lines+markers",
name = "Italy"
) %>%
plotly::layout(
title = "",
legend = list(x = 0.1, y = 0.9),
yaxis = list(title = "Number of new cases"),
xaxis = list(title = "Date"),
# paper_bgcolor = "black",
# plot_bgcolor = "black",
# font = list(color = 'white'),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)df_EU <- coronavirus %>%
# dplyr::filter(date == max(date)) %>%
dplyr::filter(Country.Region == "Bangladesh" |
Country.Region == "France" |
Country.Region == "Italy" |
Country.Region == "Spain") %>%
dplyr::group_by(Country.Region, type) %>%
dplyr::summarise(total = sum(cases)) %>%
tidyr::pivot_wider(
names_from = type,
values_from = total
) %>%
# dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
dplyr::arrange(confirmed) %>%
dplyr::ungroup() %>%
dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
dplyr::mutate(country = trimws(country)) %>%
dplyr::mutate(country = factor(country, levels = country))
plotly::plot_ly(
data = df_EU,
x = ~country,
# y = ~unrecovered,
y = ~ confirmed,
# text = ~ confirmed,
# textposition = 'auto',
type = "bar",
name = "Confirmed",
marker = list(color = active_color)
) %>%
plotly::add_trace(
y = ~death,
# text = ~ death,
# textposition = 'auto',
name = "Death",
marker = list(color = death_color)
) %>%
plotly::layout(
barmode = "stack",
yaxis = list(title = "Total cases"),
xaxis = list(title = ""),
hovermode = "compare",
margin = list(
# l = 60,
# r = 40,
b = 10,
t = 10,
pad = 2
)
)